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您的检索式：作者名="Michael M.Little"
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| 1 | Adopting GPU computing to support DL-based Earth science applications显示文摘With the advancement of Artificial Intelligence(Al)technologies and accumulation of big Earth data,Deep Learning(DL)has become an important method to discover patterns and understand Earth science processes in the past several years.While successful in many Earth science areas,Al/DL applications are often challenging for computing devices.In recent years,Graphics Processing Unit(GPU)devices have been leveraged to speed up Al/DL applications,yet computational performance still poses a major barrier for DL-based Earth science applications.To address these computational challenges,we selected five existing sample Earth science Al applications,revised the DL-based models/algorithms,and tested the performance of multiple GPU computing platforms to support the applications.Application softwarepackages,performance comparisonsacross different platforms,along with other results,are summarized.This article can help understand how various Al/ML Earth science applications can be supported by GPU computing and help researchers in the Earth science domain better adopt GPU computing(such as supermicro,GPU clusters,and cloud computing-based)for their Al/ML applications,and to optimize their science applications to better leverage the computing device. | Zifu Wang Yun Li Kevin Wang Jacob Cain Mary Salami Daniel Q.Duffy Michael M.Little Chaowei Yang | 2023 | International Journal of Digital Earth2023，16，1： | 0 |
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